Evolution of the semiconductor manufacturing industry is placing ever greater demands on yield management and, in particular, on metrology and inspection systems. Critical dimensions continue to shrink, yet the industry needs to decrease the time for achieving high-yield, high-value production. Minimizing the total time from detecting a yield problem to fixing it determines the return-on-investment for a semiconductor manufacturer.
Fabricating semiconductor devices, such as logic and memory devices, typically includes processing a semiconductor wafer using a large number of fabrication processes to form various features and multiple levels of the semiconductor devices. For example, lithography is a semiconductor fabrication process that involves transferring a pattern from a reticle to a photoresist arranged on a semiconductor wafer. Additional examples of semiconductor fabrication processes include, but are not limited to, chemical-mechanical polishing (CMP), etch, deposition, and ion implantation. Multiple semiconductor devices may be fabricated in an arrangement on a single semiconductor wafer and then separated into individual semiconductor devices.
Defect review for advanced design rules can search for objects that are quite small (e.g., for detection of defects in 10 nm range), so hot scans may be run to catch such defects. A “hot scan” generally refers to a measurement/inspection of a wafer performed to detect defects or take measurements on the wafer by applying relatively aggressive detection settings (e.g., thresholds substantially close to the noise floor). In this manner, the hot scan may be performed to collect inspection or measurement data about the wafer that will be used for the tuning process (e.g., optics selection and algorithm tuning). The goal of the hot scan may be to detect a representative sample of all defect and nuisance types on the wafer in the selected mode(s).
Repeater defects are a concern to semiconductor manufacturers. Repeater defects are those defects that appear on a wafer with some regular periodicity and that show some fixed relationship to the die layout on a reticle or stepping pattern on a wafer. Reticle defects are a common cause of repeater defects. Reticle defects that can cause repeater defects include, for example, extra chrome pattern on a mask plate, missing chrome on a mask plate, particulates on the mask plate or on the reticle, and damage to the pellicle.
Repeater filtering (e.g., with coordinates matching) can be a strong filter that can bring the nuisance density to manageable levels. However, hot inspections required for mask qualification may result in billions of defect candidates. It should be noted that repeater defects can be “soft” repeaters. Soft repeaters are not printed in every reticle due to process variation. This means that it may not be possible to use in-job repeater defect detection (RDD) while being able to analyze results for the whole wafer.
With feature shrink and a potential resolution limit for optical wafer inspection tools, the primary candidate inspection tool for print check is an electron beam inspection tool, such as a scanning electron microscope (SEM). However, electron beam inspection tools have a throughput disadvantage. With the best scenario of multiple beam/column options, the estimated inspection time for one reticle is more than eight hours. Broadband plasma (BBP) tools have much higher throughput and, hence, coverage. In the current BBP tool design, repeater analysis is part of the post-processing step in the high level defect detection controller and a current implementation of RDD supports up to 10 billion defects for initial defect detection.
Current defect detection algorithms perform defect detection in a chronologic way meaning the algorithms inspect swath after swath without ever coming back to the previous swath to apply the learning of the new swath to the old one. Current methods, such as multi-die adaptive threshold (MDAT), standard reference die (SRD), or NanoMDAT, have similar disadvantages. First, the nuisance rate is high and the repeater capture rate is low. Second, these techniques do not use information that the defects are repeater defects to improve detectability. Third, many parameters need to be used for time-consuming nuisance tuning.
These three previous techniques also have disadvantages compared to individual algorithms. MDAT and NanoMDAT both require double detection for single die-to-die comparisons for every additional defect that needs to be detected. SRD and MDAT both calculate noise from the entire image frame, which is usually 1 k×1 k pixels2. The noise can have a low signal-to-noise ratio because there can be many noise sources within an image frame.
Therefore, new repeater defect detection techniques and systems are needed.